• Login
    View Item 
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Dissertations
    • View Item
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UA Campus RepositoryCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournal

    My Account

    LoginRegister

    About

    AboutUA Faculty PublicationsUA DissertationsUA Master's ThesesUA Honors ThesesUA PressUA YearbooksUA CatalogsUA Libraries

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Using data mining in educational research: A comparison of Bayesian network with multiple regression in prediction

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    azu_td_3119990_sip1_m.pdf
    Size:
    4.596Mb
    Format:
    PDF
    Download
    Author
    Xu, Yonghong
    Issue Date
    2003
    Keywords
    Education, Tests and Measurements.
    Education, Educational Psychology.
    Advisor
    Sabers, Darrell L.
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Advances in technology have altered data collection and popularized large databases in areas including education. To turn the collected data into knowledge, effective analysis tools are required. Traditional statistical approaches have shown some limitations when analyzing large-scale data, especially sets with a large number of variables. This dissertation introduces to educational researchers a new data analysis approach called data mining, an analytic process at the intersection of statistics, databases, machine learning/artificial intelligence (AI), and computer science, that is designed to explore large amounts of data to search for consistent patterns and/or systematic relationships between variables. To examine the usefulness of data mining in educational research, one specific data mining technique--the Bayesian Belief Network (BBN) based in Bayesian probability--is used to construct an analysis model in contrast to the traditional statistical approaches to answer a pseudo research question about faculty salary prediction in postsecondary institutions. Four prediction models--a multiple regression model with theoretical variable selection, a regression model with statistical variable extraction, a data mining BBN model with wrapper feature selection, and a combination model that used variables selected by the BBN in a multiple regression procedure--are expounded to analyze a data set called the National Survey of Postsecondary Faculty 1999 (NSOPF:99) provided by the National Center of Educational Services (NCES). The algorithms, input variables, final models, outputs, and interpretations of the four prediction models are presented and discussed. The results indicate that, with a nonmetric approach, the BBN can effectively handle a large number of variables through a process of stochastic subset selection; uncover dependence relationships among variables; detect hidden patterns in the data set; minimize the sample size as a factor influencing the amount of computations in data modeling; reduce data dimensionality by automatically identifying the most pertinent variable from a group of different but highly correlated measures in the analysis; and select the critical variables related to a core construct in prediction problems. The BBN and other data mining techniques have drawbacks; nonetheless, they are useful tools with unique advantages for analyzing large-scale data in educational research.
    Type
    text
    Dissertation-Reproduction (electronic)
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Educational Psychology
    Degree Grantor
    University of Arizona
    Collections
    Dissertations

    entitlement

     
    The University of Arizona Libraries | 1510 E. University Blvd. | Tucson, AZ 85721-0055
    Tel 520-621-6442 | repository@u.library.arizona.edu
    DSpace software copyright © 2002-2017  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.